Volume 13 | Issue 5 |

 Abstract

Human civilization, today, is the offshoot of millions of years of an evolutionary process. Nature has provided us with all the necessary sources of sustenance. But the human beings have jeopardised their existence when they became greedy to the point of over-exploiting nature. Capitalism, with its ideological corollary, 'individualism' fuels consumerism, leading to ecological endangerment. The present essay proposes the birth of the 'Decent Man/ Woman', who is spiritually strong so as to differentiate between need and greed, being reasonable enough to judge that many a human project brings disaster to us in the name of 'development'. The present piece puts stress on 'behavioural modification' through the 'principle of reinforcement' following the ideas of B.F. Skinner, who speaks of 'organised conditioning'.

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 Keywords

Evolution, Capitalism, Globalisation, Economic Liberalisation, Social Darwinism, organised conditioning.

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 Abstract

Stroke is a leading threat to human life and lasting disability worldwide thus needing immediate access to accurate early prediction systems. Research explores stroke brain prediction through machine learning using publicly accessible medical data that contains information about patient demographics and lifestyle along with clinical data. The medical dataset showcases an extreme class imbalance where stroke cases made up only 5% of recorded whole data. SMOTE was used to balance the dataset therefore improving stability during model training. The proposed feature preprocessing methodology along with categorical encoding with robust scaling through RobustScaler and employ SHAP (SHapley Additive exPlanations) analysis for explainable feature selection. Through a GridSearchCV process the XGBoost classifier reached 75.77% accuracy along with 0.76 ROC-AUC score. The SHAP identification process discloses work type together with BMI and glucose levels and smoking history as important prediction variables. The implementation of this model provided accurate performance measurements of both classes through solid visual diagrams from confusion matrix and ROC curve outputs. The proposed framework offers an efficient and interpretable stroke risk prediction solution which allow healthcare practitioners to detect early high-risk subjects.

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 Keywords

Brain Stroke Prediction, Machine Learning, XGBoost, SMOTE, SHAP.

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 Abstract

Artificial intelligence plays a vital role for advancing decision taken through various services especially for healthcare decisions to make analyses, solving and recommendations. Throughout healthcare data from patients are analyzed using machine learning and deep learning models. By identifying disease affected feature and related treatment for doctor recommendation is important for right progress medications. Traditional methods are failed to analyze the feature importance of relational properties to find the proper recommendation which affects due to improper data decisions leads poor accuracy from false rates. To resolve this problem, the paper proposed an AI-Driven Healthcare System for Doctor Recommendation based on deep vectorized Grey Wolf Optimizer (GWO) with Resnet50- Convolution Neural Network for identifying disease related treatment and doctor recommendation. The initially the Z score normalization is carried out to healthcare dataset to normalize the dataset with verified margins. Then disease relational impact score is analyzed using support deep vector scar function. depending on the feature impact, the mutual dependencies of feature rate is evaluated with grey wolf optimization and classification is done with Resnet50- CNN of categorize the patient dependent doctors for recommendation. The implementation result proves best precision rate up to 95.2 % high, recall up to gain 94.8 % high with f1 score 96.5 % well compared to the existing system with redundant false rate. The proposed system well suitable for disease related risk analysis and recommendation for diagnosis with specialist doctor recommendation for immediate treatment depending on the risk by class.

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 Keywords

Artificial Intelligence, Healthcare, Doctor Recommendation, Deep Learning, Convolutional Neural Network, Grey Wolf Optimizer, Feature Importance, Risk Analysis.

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 Abstract

To help farmers manage crops and identify plant diseases, this Smart Agriculture Decision Support System combines GIS, IoT, and artificial intelligence technology. A CNN model analyzes plant photos that farmers provide via a web interface to detect problems and recommend treatments. For tracking, a distinct token with the user ID, timestamp, illness, and location is created. A weather dashboard can forecast hazards thanks to the real-time weather and geological data collected by IoT sensors. Through a specialized interface, administrators keep an eye on epidemics and send out alerts, encouraging early intervention, resource optimization, and environmentally friendly agricultural methods.

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 Keywords

IoT, CNN, Plant Disease Detection, Disease Prevention, Smart Agriculture, AgriShield, Deep Learning, Tomato, Potato, Corn, Web App.

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The study focuses on identifying antibiotic resistance genes (ARGs) in Gram-positive bacteria from semen samples with confirmed bacteriospermia. PCR was used to detect resistance genes, and antimicrobial susceptibility testing helped correlate genotypic findings with phenotypic resistance. The goal was to assess the implications of these bacteria on male infertility and potential transmission through assisted reproductive technologies (ARTs). The results showed a significant presence of ARGs, underlining the importance of routine screening and judicious antibiotic use in reproductive health.

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 Keywords

PCR ,Antibiotic resistance genes, Gram-positive bacteria , Bacteriospermia , Semen samples, Molecular detection' Resistance profiling Clinical microbiology, Male infertility , Pathogen identification.

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 Abstract

The coastal communities of India have preserved profound ecological wisdom through their literary and oral traditions, articulated most powerfully through the ancient Tamil concept of Tina. This framework, particularly its Neytal(coastal) manifestation, reveals an intricate understanding of marine ecosystems that has sustained fishing communities for generations. Classical Tamil texts like Tolkappiyam and Ainkurunuru establish the foundations of this worldview, while modern works such as Thakazhi Sivasankara Pillai's Chemmeen demonstrate its enduring relevance in contemporary contexts. Fisherfolk literature embodies environmental knowledge through multiple dimensions. Seasonal fishing practices mirror the rhythms of marine life, with strict adherence to monsoon bans and spawning periods. Ritual expressions, from boat-launching ceremonies to sea-worship traditions, reinforce the sacred bond between humans and the marine world. Narrative forms like work songs, proverbs, and folk tales serve as living repositories of ecological observation, encoding detailed understandings of tides, species behavior, and conservation ethics. This exploration contributes to broader conversations in environmental humanities by demonstrating how literary traditions can embody and sustain ecological knowledge. The Tinai perspective offers crucial insights for addressing contemporary challenges facing coastal ecosystems, suggesting that solutions may lie in the very traditions that have maintained these relationships for centuries.

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Tinai theory, fisherfolk literature, ecological wisdom, oral traditions, sustainable practices

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 Abstract

Tuberculosis (TB) remains a critical global health issue, causing approximately 10 million new cases and 1.5 million deaths annually. Despite the availability of treatment, TB control faces persistent challenges, especially in resource-limited settings. This review examines the nature of TB, including its types, symptoms, and the increasing threat posed by multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB). It compares traditional diagnostic methods--such as smear microscopy, culture-based tests, the tuberculin skin test, and chest X-rays--with innovative diagnostic tools like GeneXpert MTB/RIF. The review also explores factors affecting treatment adherence, including socioeconomic, psychological, and systemic barriers, particularly in cases of latent TB. Furthermore, it highlights emerging digital health solutions aimed at improving adherence and monitoring treatment. By evaluating the effectiveness, feasibility, and scalability of both conventional and modern strategies, this article offers insights for policymakers and healthcare providers to enhance TB control, diagnosis, and treatment outcomes globally.

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 Keywords

Tuberculosis, digital Technology, TB, MTB

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 Abstract

Disney fairy tale movies have created a huge impact on children and society. It has evolved a lot over the years, but not always for the better. In the past, the stories were all about beautiful princesses waiting to be saved by a prince. The messages were often misogynistic and women had to be quiet, kind, and pretty, and their lives only got better when they got married. Movies like Snow White, Cinderella, and Sleeping Beauty portrayed girls as passive and helpless. Even when Disney tried to improve things in the 1990s, like in The Little Mermaid or Beauty and the Beast, love still happened at first sight and the female characters still gave up a lot for men. In recent years Disney has gone the other way. Now it's all about being independent, strong, and not needing anyone. While that sounds good on the surface level it feels forced or unnatural. Characters like Elsa in Frozen II, or the new Ariel in the 2023 Little Mermaid remake are written to be perfect from the start. They don't fall in love; they don't make real mistakes and they don't grow. Romance is seen as a weakness and emotional moments are made as empowerment. This paper looks at how Disney moved from one extreme to another by first making stories that were too old fashioned and now making stories that try too hard to be modern. Both styles miss the mark when it comes to real people and real feelings. The best Disney stories like Moana and Tangled show characters who are strong but also emotional, brave but also flawed. This paper argues that Disney does not need to be perfect or "woke", it just needs to be real.

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 Keywords

Disney, Fairy Tales, Movies, Woke, independent, Little Mermaid, Snow White, Woke

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Abstract This study aims to understand the key challenges faced by fish farmers in Kerala, particularly in the Alappuzha district. Fish farmers in Kerala play a crucial role in sustaining the state's aquaculture sector, which significantly contributes to food security, rural income, and employment. Despite Kerala's natural advantages--such as abundant water resources and a long coastline--farmers encounter numerous obstacles. Unpredictable weather conditions, especially excessive rainfall and flooding, frequently damage farming infrastructure and lead to loss of fish stock. Rising input costs, limited access to affordable credit, lack of adequate insurance coverage, and insufficient institutional support further intensify the difficulties. Although various government schemes are in place, many farmers report challenges in accessing these benefits and question their effectiveness. Nevertheless, the sector demonstrates resilience and adaptability, with growing interest in sustainable practices and community-based fish farming cooperatives.

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Keywords: Fish Farmer, Aquaculture, Input Cost, Flood Impact, Salinity Intrusion

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Road safety has become a critical concern in today's world. This project focuses on enhancing safety by leveraging machine learning and deep learning techniques to monitor and classify driver behavior in real time. It recognizes ten different driver activities, including safe driving, texting, phone calls, drinking, and others. The system employs advanced Convolutional Neural Networks (CNNs), transfer learning models such as VGG16 and ResNet50, and object detection using YOLOv8. A key feature is a drowsiness detection module designed to alert drivers showing signs of fatigue. The model is trained and evaluated using the State Farm Distracted Driver Detection dataset, and real-time monitoring is facilitated through a Flask-based web application that delivers alerts. Performance metrics including Accuracy, Precision, Recall, and F1-score confirm the system's high effectiveness in promoting driver alertness and reducing accident risks. This solution is well-suited for integration into modern vehicle safety systems and fleet management platforms. The drowsiness detection component is trained on standard datasets distinguishing open and closed eyes, enabling real-time identification of fatigue and providing timely alerts to enhance overall road safety.

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 Keywords

Driver Assistance System, Road Safety, Deep Learning, Convolutional Neural Networks (CNN), VGG16, ResNet50, YOLOv8, Drowsiness Detection, Real-Time Monitoring

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Creative Commons Attribution 4.0 and The Open Definition